In recent years, numerous screening methods have been published for
ultrahigh-dimensional data that contain hundreds of thousands of features;
however, most of these features cannot handle data with thousands of classes.
Prediction models built to authenticate users based on multichannel biometric
data result in this type of problem. In this study, we present a novel method
known as random forest-based multiround screening (RFMS) that can be
effectively applied under such circumstances. The proposed algorithm divides
the feature space into small subsets and executes a series of partial model
builds. These partial models are used to implement tournament-based sorting and
the selection of features based on their importance. To benchmark RFMS, a
synthetic biometric feature space generator known as BiometricBlender is
employed. Based on the results, the RFMS is on par with industry-standard
feature screening methods while simultaneously possessing many advantages over
these methods.

本研究提出了一种名为基于随机森林多轮筛选 (RFMS) 的新方法，它可以有效地应用于多通道生物特征数据的身份验证问题，并在许多方面具有行业标准特征筛选方法的优势。

基于随机森林多轮筛选的超高维多类别数据特征空间降维方法（RFMS）

Feature space reduction method for ultrahigh-dimensional, multiclass  data: Random forest-based multiround screening (RFMS)

Learning expressive low-dimensional representations of ultrahigh-dimensional
data, e.g., data with thousands/millions of features, has been a major way to
enable learning methods to address the curse of dimensionality. However,
existing unsupervised representation learning methods mainly focus on
preserving the data regularity information and learning the representations
independently of subsequent outlier detection methods, which can result in
suboptimal and unstable performance of detecting irregularities (i.e.,
outliers).
This paper introduces a ranking model-based framework, called RAMODO, to
address this issue. RAMODO unifies representation learning and outlier
detection to learn low-dimensional representations that are tailored for a
state-of-the-art outlier detection approach - the random distance-based
approach. This customized learning yields more optimal and stable
representations for the targeted outlier detectors. Additionally, RAMODO can
leverage little labeled data as prior knowledge to learn more expressive and
application-relevant representations. We instantiate RAMODO to an efficient
method called REPEN to demonstrate the performance of RAMODO.
Extensive empirical results on eight real-world ultrahigh dimensional data
sets show that REPEN (i) enables a random distance-based detector to obtain
significantly better AUC performance and two orders of magnitude speedup; (ii)
performs substantially better and more stably than four state-of-the-art
representation learning methods; and (iii) leverages less than 1% labeled data
to achieve up to 32% AUC improvement.

RAMODO 是一个基于排名模型的框架，旨在通过 REPEN 方法来解决编码表示和异常检测之间的不一致性问题，充分利用少量的标注数据来提高表征学习的性能和稳定性。